A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation

With the increasing global demands for energy, fuel supply management is a challenging task of today’s industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations

An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time

Modelos de otimização para a distribuição de combustíveis em curta distância marítima

Doutoramento em Matemática e AplicaçõesO transporte marítimo e o principal meio de transporte de mercadorias em todo o mundo. Combustíveis e produtos petrolíferos representam grande parte das mercadorias transportadas por via marítima. Sendo Cabo Verde um arquipelago o transporte por mar desempenha um papel de grande relevância na economia do país. Consideramos o problema da distribuicao de combustíveis em Cabo Verde, onde uma companhia e responsavel por coordenar a distribuicao de produtos petrolíferos com a gestão dos respetivos níveis armazenados em cada porto, de modo a satisfazer a procura dos varios produtos. O objetivo consiste em determinar políticas de distribuicão de combustíveis que minimizam o custo total de distribuiçao (transporte e operacões) enquanto os n íveis de armazenamento sao mantidos nos n íveis desejados. Por conveniencia, de acordo com o planeamento temporal, o prob¬lema e divido em dois sub-problemas interligados. Um de curto prazo e outro de medio prazo. Para o problema de curto prazo sao discutidos modelos matemáticos de programacao inteira mista, que consideram simultaneamente uma medicao temporal cont ínua e uma discreta de modo a modelar multiplas janelas temporais e taxas de consumo que variam diariamente. Os modelos sao fortalecidos com a inclusão de desigualdades validas. O problema e então resolvido usando um "software" comercial. Para o problema de medio prazo sao inicialmente discutidos e comparados varios modelos de programacao inteira mista para um horizonte temporal curto assumindo agora uma taxa de consumo constante, e sao introduzidas novas desigualdades validas. Com base no modelo escolhido sao compara¬das estrategias heurísticas que combinam três heur ísticas bem conhecidas: "Rolling Horizon", "Feasibility Pump" e "Local Branching", de modo a gerar boas soluçoes admissíveis para planeamentos com horizontes temporais de varios meses. Finalmente, de modo a lidar com situaçoes imprevistas, mas impor¬tantes no transporte marítimo, como as mas condicões meteorológicas e congestionamento dos portos, apresentamos um modelo estocastico para um problema de curto prazo, onde os tempos de viagens e os tempos de espera nos portos sao aleatórios. O problema e formulado como um modelo em duas etapas, onde na primeira etapa sao tomadas as decisões relativas as rotas do navio e quantidades a carregar e descarregar e na segunda etapa (designada por sub-problema) sao consideradas as decisoes (com recurso) relativas ao escalonamento das operacões. O problema e resolvido por um metodo de decomposto que usa um algoritmo eficiente para separar as desigualdades violadas no sub-problema.Maritime transportation is a major mode of transportation of goods worldwide. Most of cargo of the maritime transport accounted for liquid cargo oil and petroleum products. As Cape Verde is an archipelago, maritime transportation is of great importance for the local economic activity. We consider a fuel oil distribution problem where an oil company is responsible for the coordination of the distribution of oil products with the inventory management of those products at ports in order to satisfy the demands for the several oil products. The objective is to determine distribution policies that minimize the routing and operating costs, while inventory levels are maintained within given limits. For convenience, the planning problem is divided into two related subproblems accordingly to the length of the planning horizon: A short- term and medium-term planning. For the short-term planning problem we discuss mathematical mixed integer programming models that combine continuous and discrete time measures in order to handle with multiple time windows and a daily varying consumption rate of the various oil products. These models are strengthened with valid inequalities. Then the problem is solved using a commercial software. For the second subproblem several mixed integer formulations are discussed and compared for a short time horizon, and assuming constant consumption rates and new valid inequalities are introduced. Then, based on the chosen model, we compare several heuristic strategies that combine the well-known Rolling Horizon, Feasibility Pump and Local Branching heuristics, in or¬der to derive good feasible solutions for planning horizons of several months. Finally, as weather conditions and ports congestion are very impor¬tant in maritime transportation, we present a stochastic model for a short sea shipping problem, where traveling and waiting time are random. The problem is formulated as a two stage recourse problem, where in the first stage the routing and the load/unload quantities are defined, and in the second stage (subproblem) the scheduling of operations is determined. The problem is solved by a decomposition method that uses an efficient separation algorithm to include inequalities from the subproblem

Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

Cheapest Insertion Constructive Heuristic based on Two Combination Seed Customer Criterion for the Capacitated Vehicle Routing Problem

The heuristic method is a well-known constructive method for initialize trail quality solutions in capacitated vehicle routing problem. Cheapest insertion heuristic is a popular construction heuristic known for being fast, producing decent solutions, simple to implement and easy to extend handling complicated constraints. However, in previous work, there was less focus on diverse initial quality solutions. Therefore, this study proposed an extension to the cheapest insertion heuristic which consider various combinations of seed customer criteria (the first customer inserted on a route) to preserve solutions diversification. Three seed customer criteria proposed which based on the combination of two criteria based on (farthest, nearest and random criteria). The best performing criteria selected and tested on benchmark dataset, later compared with Clarke and Wright saving heuristic. The results shown that the combination of (farthest and random) criteria obtained the best initial solution which preserve balance between the quality and diversity, with less time when compared to Clarke and wright saving heuristic. This approach is for generating diverse and quality starting solutions for the capacitated vehicle routing problem

Greedy approach for solving the capacitated vehicle routing problem of liquefied natural gas distribution to power plants

In LNG industries, how to decide the number of ships and their routes for transporting LNG to every demand location efficiently effects the minimization of total operational cost. Therefore, this paper provides a case study in Papua and proposes a model to determine the optimum ship route to transport LNG from an LNG production terminal to thirteen regasification terminals by considering both transportation cost and inventory cost. Distance, power plants demands, transportation cost, and inventory cost were further analyzed by using the greedy approach. In addition, the ship sizes were limited to four alternatives, which were 2500 m3, 7500 m3, 10000 m3, and 23000 m3. The result recommends the utilization of smaller size vessels with more frequent shipments compared to the earlier research on the same case study. It considers that the result will be more adaptable for changing water depth due to changing tides at particular ports

TOOLS TO SUPPORT TRANSPORTATION EMISSIONS REDUCTION EFFORTS: A MULTIFACETED APPROACH

The transportation sector is a significant contributor to current global climatic problems, one of the most prominent problems that today's society faces. In this dissertation, three complementary problems are addressed to support emissions reduction efforts by providing tools to help reduce demand for fossil fuels. The first problem addresses alternative fuel vehicle (AFV) fleet operations considering limited infrastructure availability and vehicle characteristics that contribute to emission reduction efforts by: supporting alternative fuel use and reducing carbon-intensive freight activity. A Green Vehicle Routing Problem (G-VRP) is formulated and techniques are proposed for its solution. These techniques will aid organizations with AFV fleets in overcoming difficulties that exist as a result of limited refueling infrastructure and will allow companies considering conversion to a fleet of AFVs to understand the potential impact of their decision on daily operations and costs. The second problem is aimed at supporting SOV commute trip reduction efforts through alternative transportation options. This problem contributes to emission reduction efforts by supporting reduction of carbon-intensive travel activity. Following a descriptive analysis of commuter survey data obtained from the University of Maryland, College Park campus, ordered-response models were developed to investigate the market for vanpooling. The model results show that demand for vanpooling in the role of passenger and driver have differences and the factors affecting these demands are not necessarily the same. Factors considered include: status, willingness-to-pay, distance, residential location, commuting habits, demographics and service characteristics. The third problem focuses on providing essential input data, origin-destination (OD) demand, for analysis of various strategies, to address emission reduction by helping to improve system efficiency and reducing carbon-intensive travel activity. A two-stage subarea OD demand estimation procedure is proposed to construct and update important time-dependent OD demand input for subarea analysis in an effort to overcome the computational limits of Dynamic Traffic Assignment (DTA) methodologies. The proposed method in conjunction with path-based simulation-assignment systems can provide an evolving platform for integrating operational considerations in planning models for effective decision support for agencies that are considering strategies for transportation emissions reduction

Tabu search for ship routing and scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2006.This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems